Systems and methods for mitigating spoofing of vehicle features

ABSTRACT

Systems and methods for mitigating certain spoofing of vehicle features are disclosed herein. An example method can include determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time, determining road disturbances using a road disturbance model, applying a driver model that is indicative of human driver hands-on-wheel behaviors, determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model, and executing a remediating measure when the input torque values are indicative of the spoof.

BACKGROUND

A spoof is an action taken to mimic or masquerade. In the context ofvehicles, a spoof can involve an action that is intended to mimic driveror passenger behavior(s) for a particular purpose. Some spoofs may becaused by vehicle occupants in order to override certain vehiclefeatures. For example, a vehicle may have advanced driver assistancesystem (ADAS) features such as full or partial steering assistance,automatic braking, and so forth providing driver assistance, partial andconditional automation of driving tasks. In some instances, usage ofthese features can require periodic driver input. For example, periodicdriver input to a steering wheel, indicating that the driver's hands areon the steering wheel, may be required for the continual use of steeringassistance features by ensuring driver alertness, awareness of drivingenvironment, and ability to resume some or all driving functions. Incertain instances, these features may be primarily overridden by asudden driver input or disengaged when conditions for the feature arenot met. For example, an accident-avoidance maneuver or transitioning toa road with poor lane markings. A driver may be tempted to spoof theADAS system with input that mimics the expected input. However, spoofedinput can cause unintended effects.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description is set forth regarding the accompanying drawings.The use of the same reference numerals may indicate similar or identicalitems. Various embodiments may utilize elements and/or components otherthan those illustrated in the drawings, and some elements and/orcomponents may not be present in various embodiments. Elements and/orcomponents in the figures are not necessarily drawn to scale. Throughoutthis disclosure, depending on the context, singular and pluralterminology may be used interchangeably.

FIG. 1 illustrates an example environment where aspects of the presentdisclosure may be practiced.

FIGS. 2A and 2B is a graph of torsional response of a steering columnover time using a dynamic solver.

FIG. 3 is a graph illustrating various steering wheel input responsesunder various conditions.

FIG. 4 is another graph illustrating various steering wheel inputresponses under different conditions.

FIG. 5 is a flowchart of an example method of the present disclosure.

DETAILED DESCRIPTION Overview

The present disclosure pertains to the detection and mitigation ofcertain spoof events related to vehicle operations. For example, systemsand methods can be used to determine hands-on wheel (HONW) spoofing invehicles. When human hands are placed on a steering wheel, they applytorque that can be detected by sensors in the steering column even whenthe driver's input does not diverge from the present steering wheelangle. ADAS systems often require periodic confirmation of driver handson wheel based on torque sensing. Often ADAS features incorporateautomated steering where the driver is required to maintain periodichands on wheel detection but does not intend to deactivate ADAS throughsteering inputs that significantly diverge from the ADAS feature input.Hands-on-wheel detection may be further confounded by torque induced inthe steering assembly from road disturbances as well as by spoofingevents, such as applying steering wheel torque through affixing objectsto the steering wheel or by applying resistive torque to the steeringwheel through friction on the steering wheel and an object. To preventthis type of spoofing, an example system leverages static and dynamicproperties of the steering wheel assembly that can be measured andinterrogated during vehicle usage. In addition to the steering wheelassembly, other vehicle components responsible, directly or indirectly,for vehicle steering can be monitored.

An example method may include monitoring input signals to the steeringassembly, which are likely generated inside the vehicle by a driver orother objects. These inputs can be measured over time and used forhistorical analyses. The method can also include detecting roadconditions (e.g., road disturbances), which may apply external orindirect input to the steering assembly. Vehicle acceleration, turning,or other noise may cause a change in sensed torque and/or steering anglerelative to the steering assembly.

An example system can detect and filter noise using methods known in thearts as well as incorporating vehicle sensors to predict torque-on-wheel(or other equivalents based on steering input mechanism type) values.For example, the system can feed a road profile and speed signals into aclassical algorithm, neural network (NN) or other machine-learningalgorithm to create a baseline road disturbance torque on the wheel anddetermine how those data change over time. An example system or methodcan apply a pre-generated steering model. Generally, there is a definedresponse at the steering wheel and there is an expected reaction fromthe driver. Larger vehicle events can be evaluated to differentiatedriver hands on wheel (HONW) events from spoofing attacks. Again, ananalytical model, neural network, gradient boosted tree, anomalydetection algorithms, and the like may be used. Statisticaltransformations and/or tests may be applied as well.

A driver behavior model can be used in some instances. Some drivers mayapply different magnitudes and distributions of torque to the steeringwheel under varying time intervals. These driving inputs may be chaotic,correlated with driving task, and/or representative of a specific driverbehavior. The vehicle may predict a path plan based on a human-baseddriver model using vision and/or sensor fusion/tracking data (e.g.,time-to-collision (TTC), occupancy grid, dynamic occupancy grid) andpredict expected torque-on-wheel to achieve a general or driver-specificpath plan.

The systems and methods can utilize a spoofing, HONW, hands-off-wheel(HOFW) algorithm (could be an analytical model or other equivalent). Thealgorithm can include any one or more of an analytical equation, neuralnetwork, gradient boost tree algorithm, and so forth—just to name a few.The algorithm can be seeded and improved using training data frommultiple drivers and driver behaviors as well as data from differenttypes of spoofing solutions (e.g., use of a water bottle, weight, toy(e.g., rubber duckie), etc.). Generally, it is assumed that a torqueprofile of human driver input and that of a spoofing object isfundamentally different (random/chaotic/correlated with driving task vsfunctional output based on time history). Further, a spoofing objectwould likely show a repeatable set of behaviors based on physicalparameters in the environment such as vehicle acceleration, steeringwheel angle, wheel angle change, road crown, vehicle speed, lateralacceleration, yaw rate and so forth. Further, a driver may choose toleave a spoofing device always attached to the wheel, during both manualand automated driving, where the wheel torque input would be additive ofthe driver input and spoofing device.

Spoofing technologies may incorporate motors to cause a time-varyingquasi-random torque. This may be detected using audio detection ofpotential spoofing noises. The sounds may be localized based on amicrophone array and steering angle. Capacitive sensors in the steeringwheel may also be used to detect time-varying signals of quasi-randomtorque. The weakness of this spoofing attack is that it would beinsensitive to road environments (e.g., the driver being nervous andmoving/adjusting their hands on the steering wheel, requests to touchwheel, vehicle HMI inputs) or vehicle requests. Additionally, it wouldnot reflect the specific driver behavior characteristic of the presentdriver, such as time between wheel inputs, wheel input magnitude, etc.In some instances, the ADAS can cause the display of a warning thatinforms the driver to place hands on the steering wheel to confirm achange in the torque. An example system or method can execute aremediating measure such as alert driver as to current torqueconditions, slow down the vehicle, deactivate ADAS features, and thelike. In one example, a warning can be displayed on a human-machineinterface (HMI). For example, if the vehicle is in a fully orsemi-autonomous mode of steering where the ADAS executing steeringmaneuvers to center the vehicle in a lane during travel, the ADAS maywarn the driver that the vehicle will be taken out of the fully orsemi-autonomous mode of steering.

The Society of Automotive Engineers (SAE) defines six levels of drivingautomation ranging from Level 0 (fully manual) to Level 5 (fullyautonomous). These levels have been adopted by the U.S. Department ofTransportation. Level 0 (L0) vehicles are manually controlled vehicleshaving no driving related automation. Level 1 (L1) vehicles incorporatesome features, such as cruise control, but a human driver retainscontrol of most driving and maneuvering operations, Level 2 (L2)vehicles are partially automated with certain driving operations such assteering, braking, and lane control being controlled by a vehiclecomputer. The driver retains some level of control of the vehicle andmay override certain operations executed by the vehicle computer, Level3 (L3) vehicles provide conditional driving automation but are smarterin terms of having an ability to sense a driving environment and certaindriving situations. Level 4 (L4) vehicles can operate in a self-drivingmode and include features where the vehicle computer takes controlduring certain types of equipment events. The level of humanintervention is very low. Level 5 (L5) vehicles are fully autonomousvehicles that do not involve human participation. The ADAS can beconfigured to allow any or all of these levels of autonomous operation,as well as restrict these modes to mitigate a spoof.

It will be understood that a spoofing attack may have a specific set offunctional relationships between the vehicle state and torque sensors.On the other hand, the driver torque input can be quasi-chaotic wherethe torque amount varies, as well as timing. A driver model and pathprediction model may be used to correlate torque inputs with predicteddriving behavior. Even in the worst-case scenario of a spoofing attackby a device that applies random torque values intermittently, suchevents may be detected as these events add a physical weight to someportion of the steering wheel that can be detected, even in the presenceof noise created by random torque inputs. Further, the torque inputs maynot be correlated with human driving predictions. A torsional responseof steering column over time using a dynamic solver (useful for ahanging weight swinging, inertia of steering wheel, and the like) can beused to identify a spoof.

Illustrative Embodiments

Turning now to the drawings, FIG. 1 depicts an illustrative architecture100 in which techniques and structures of the present disclosure may beimplemented. The architecture 100 includes a vehicle 102 that comprisesan ADAS 104, a steering wheel assembly 106, a HONW sensor(s) 108, avehicle sensor platform 110, and a modeling engine 112.

Generally, a driver 101 is operating the vehicle 102. The driver 101 mayattempt to spoof input to the steering wheel assembly 106 by associatingan object 103 with the steering wheel assembly 106. The steering wheelassembly 106 can include a steering wheel coupled with a steeringlinkage, but is not so limited. The steering wheel assembly 106 couldinclude a yoke, a joystick, or other similar steering input mechanism.In some instances, the steering wheel assembly 106 may be referred to asa steering input mechanism.

The driver 101 may want to mimic HONW input to the steering wheelassembly 106 that would ordinarily be used by the ADAS 104 to enableautonomous driving features. That is, the ADAS 104 may enable a semi- orcompletely autonomous driving mode where the output from the vehiclesensor platform 110 can be used to automatically steer the vehicle 102.The ADAS 104 can also provide steering guidance or other levels ofautonomy. In some instances, activation and/or continued use of theseautonomous features may be based on the driver having their hands on thesteering wheel.

The ADAS 104 can comprise a processor 114 and memory 116. The processor114 executes instructions stored in memory 116 to perform any of themethods disclosed herein. When referring to actions performed by theADAS 104, it will be understood that this includes execution ofinstructions by the processor 114. The vehicle 102 can also comprise acommunications interface 118 that allows the ADAS 104 to access anetwork 120. The network 106 can include combinations of networks. Forexample, the network 120 may include any one or a combination ofmultiple different types of networks, such as cellular, cable, theInternet, wireless networks, and other private and/or public networks.The network 120 can include either or both short and long-range wirelessnetworks.

In some instances, a spoof attack may be lodged against the vehicle 102over the network 120. For example, a spoofer may attempt to control thevehicle 102 remotely by transmitting spoofing commands over the network120 to the vehicle 102, as will be discussed in greater detail herein.While implementations disclosed herein contemplate use of the ADAS 104to perform spoof detecting and mitigation, a standalone or dedicatedcontroller can also be used. That is, the spoof detecting and mitigationfeatures attributed to the ADAS herein can be performed by a dedicatedcontroller, which can control operations of the ASDS.

The ADAS 104 can be configured to obtain and monitor steering input fromthe steering wheel assembly 106 over time. For example, torque forcesgenerated by a hand or hands of the driver on the steering wheelassembly 106 can be sensed using the HONW sensor(s) 108. In otherexamples, HONW sensor(s) can include capacitive sensors can be used todetect when the driver's hands are on or off the wheel, as well as thevarious aspects of spoof detection disclosed above. In addition tomonitoring steering wheel torque, other parameters such as steeringwheel angle can also be measured.

The ADAS 104 can also detect road conditions using the vehicle sensorplatform 110. In general, the vehicle sensor platform 110 can include aplurality of different types of sensors such as cameras, microphones,capacitive sensors, radar, ultrasonic, LiDAR, accelerometers, and thelike. For example, road conditions and features can be identified fromimages obtained from forward-facing cameras of the vehicle sensorplatform 110. It will be understood that while cameras have beendisclosed, other similar image or object detecting sensors can be used.Road conditions can be obtained from maps or other road informationalsources. It will be understood that road conditions may create noisesuch as jarring or vibration that is sensed by the HONW sensor(s) 108.As noted above, vehicle acceleration, turning, or other noise may causea change in sensed torque and/or steering angle relative to the steeringwheel assembly. For example, the ADAS 104 can monitor the output of anonboard accelerometer of the vehicle sensor platform 110 to detectjarring from road bumps or other similar road features. In someinstances, the ADAS 104 is configured to generate a road disturbancemodel from images obtained from the camera. That is, the ADAS 104 usingthe modeling engine 112, which applies image processing logic to detectroad disturbances in the images.

Acceleration forces exerted on the vehicle and into the steering wheelassembly 106 may cause a change in sensed torque and/or steering anglethat can be detected by the ADAS 104. The ADAS 104 can be configured todetect and filter this noise using methods known in the arts as well asincorporating vehicle sensors to predict torque on the wheel. Forexample, the ADAS 104 can utilize a road profile in combination with aspeed signal. The ADAS 104 can evaluate these data by executing themodeling engine 112, which can apply a neural network to produce abaseline road disturbance torque on the wheel.

The modeling engine 112 can also be executed by the ADAS 104 to apply asteering model. In general, a steering model can comprise a measurementof steering angle, wheel angle, and lateral forces. Steering anglepertains to an angle of the steering wheel due to the driver turning thesteering wheel. Wheel angle relates to the angle of the steering wheelcolumn and/or steering wheel (e.g., tilted towards or away from thedriver). Lateral forces may be exerted on the steering wheel assemblywhen the vehicle is driven around curves or embankments at certainspeeds.

In some instances, a steering model can be used to identify distinctinteractions between road profile inputs and expected user responses. Itwill be understood that there may exist a defined response that issensed at the steering wheel and there is an expected reaction from thedriver. Evaluation of larger vehicle events would be able todifferentiate true HONW signals from spoofing events.

Referring briefly to FIGS. 1 and 2A, the ADAS 104 can also execute themodeling engine 112 to apply a driver behavior model. It will beunderstood that drivers may apply different magnitudes and distributionsof torque to the steering wheel assembly 106 under varying timeintervals. For example, present spoofing technology is typically the usea fixed weight attached to a fixed point on the steering column thatwould apply a torque to the steering wheel as a function of object mass,location on wheel, wheel angle and vehicle acceleration that is easilymodeled. FIG. 2A illustrates a table 200 of different resulting torquesand corresponding wheel rotations for spoofing object at differentlocations on the steering wheel, (3 o'clock, 5 o'clock, and 6 o'clockpositions.

The modeling engine 112 can predict a path plan based on a human drivermodel using vision and/or sensor fusion/tracking data (e.g., TTC,occupancy grid, dynamic occupancy grid). That is, the sensor output canbe obtained from the vehicle sensor platform 110 and used to predict therequired torque-on-wheel to achieve a general or driver-specific pathplan.

The ADAS 104 can detect spoofing by applying a HONW, HOFW algorithm.This algorithm can include an analytical equation, neural network,gradient boost tree algorithm, or other logical constructs. To developthe algorithm, training data can be obtained from a number of drivers.Driver behaviors and types of spoofing solutions may be used. Forexample, spoofing profiles can be generated by evaluating commonspoofing inputs to the steering wheel assembly. In one example, asillustrated in FIG. 1 , the object 103 attached to the steering wheelassembly 106 can be modeled in terms of torque values and wheel angleover time. Another example includes when a water bottle or other objectis used to apply pressure to a certain part of the steering wheel tomimic torque or angular forces produced by a human hand(s). Again, ingeneral, any spoofing type can be modeled.

It will be understood that a torque profile of a human driver and thatof a spoofing object is fundamentally different. In most instances, thetorque created by a human is random/chaotic and functional output basedon time/historical/driving environment data. A spoofing object, evenwhen motorized, would show a repeatable set of behaviors based onphysical parameters in the environment such as vehicle acceleration,steering wheel angle, wheel angle rate of change, and so forth—just toname a few.

In some instances, the ADAS 104 can be configured to detect spoofing dueto spoofing technologies incorporated into the vehicle. For example,spoofing technologies may incorporate motors to cause a time-varyingquasi-random torque. This may be detected using audio detection ofpotential spoofing noises. The sounds may be localized based on amicrophone array and steering angle. Capacitive sensors may also be usedto detect time-varying signals of quasi-random torque. It will beunderstood that capacitive contact is a function of grip strength.

The audio sensors such as microphones and capacitive sensors carincluded in the vehicle sensor platform 110. Again, the weakness of thisspoofing attack is that it would be insensitive to road environments(e.g., driver being nervous and moving/adjusting their hands on wheel)or vehicle requests. In some instances, the ADAS 104 can display awarning to instruct the driver to place their hands on the steeringwheel to confirm a change in the torque. For example, the ADAS 104 canrequest the driver to apply pressure to the steering wheel.

In one example, as best illustrated in FIG. 1 , a warning 122 can bedisplayed on a human-machine interface (HMI) 124. In one example, theremediating measure includes causing the ADAS 104 to slow the vehicle ordeactivate a fully or semi-autonomous mode of steering. For example, ifthe vehicle is in fully or semi-autonomous mode of steering where theADAS 104 executing steering maneuvers to center the vehicle in a laneduring travel, the ADAS 104 may warn the driver that the vehicle will betaken out of the fully or semi-autonomous mode of steering.

In sum, a spoofing attack typically has a specific functionalrelationship between vehicle state and torque sensors. On the otherhand, the driver torque input may typically be quasi-chaotic where thetorque amount can vary as well as timing. A driver model and pathprediction model may be used to correlate torque inputs with predicteddriving behavior. Even in the worst-case scenario of a spoofing attackby a device which applies random torque values intermittently, suchevents can be easily detected as they add a physical weight to someportion of the steering wheel which can be detected even with the“noise” of the random torque inputs. Further, the torque inputs likelywill not correlate with human driving predictions. In some instances,the ADAS 104 can be configured to detect a weight location (relativelocation on a steering wheel) and mass, as well as rotation of thesteering wheel caused by a fixed weight applied to a point on a steeringwheel or other similar spoofing techniques. Further, the properties ofthe spoofing device can be predicted such as fixed mass, viscous liquid,motorized, etc. In addition, the vehicle may set communicate to acentral server with information on spoofing device detection. In oneexample, when the vehicle is enabled with motorized steering for lanecentering, upon startup, the wheel may jerk slightly as the motorsinitiate. This initiation point may be used as a trigger to use sensingto detect a spoofing device.

In some instances, additional vehicle features can be measured and usedto determine spoofing events. For example, other types of driver sensorssuch as seat belts, seat weight sensing, seat configuration, driverstate monitoring camera, capacitive sensor, and the like can also beused to detect spoofing. In some configurations, HONW and spoofingevents can be detected and/or corroborated by fusing interior camerahand detection with HONW logic of present disclosure.

FIG. 2B is a graph 202 of torsional response of a steering column overtime using a dynamic solver. This analysis is useful in detectingspoofing events associated with a hanging weight swinging, the inertiaexerted against the steering wheel, and so forth. In the present model,a weight was applied to a steering wheel and equilibrated over 0.2seconds. The steady response after equilibrium time is in contrast tohuman input for which the force applied would have more lateralcontribution and be more chaotic in magnitude and applied direction.More complex FEA (finite element analysis) models can be used over arange of conditions (e.g., spoofing object type, wheel angle history,power steering torque response such as in ADAS).

FIG. 3 is a graph illustrating various steering wheel responses underdifferent conditions. It will be understood that graphs in FIGS. 3 and 4assume that there has been a calculation and canceling of the steeringsystem mechanical response and momentum behavior to vehicle accelerationand mechanical excitations.

In more detail, the graph illustrates instances of large and relativelyfast motion of the steering wheel and the torque response of the wheelunder various conditions. In graph segment 302, torque values areillustrated over time for human driver input. The data points, whenconnected, illustrate the quasi-chaotic nature of human driver input(HONW input). Graph segment 304 illustrates torque values when aspoofing technique is used. That is, the torque and inertial values areindicative of an object that is applying cyclical input (notquasi-random). Graph segment 306 illustrates torque values relative totime for HOFW events, where torque values are low and are indicative ofnoise (e.g., road imperfections, inertia from turning vehicle, and soforth). Also, in the HOFW condition, noise of the momentum of thesteering wheel itself is not filtered, which may be 0 after filtering.This is to show the increasing momentum of the steering wheel due to theweight on the wheel when the spoofing device is attached.

Graph segment 308 illustrates steering wheel angle over time applied byan advanced driver assistance system (e.g., power steering). It isassumed that the ADAS 104 is providing a command to move the steeringwheel under various sinusoidal patterns of different rates.

FIG. 4 is a graph that illustrates examples of limited motion ofsteering wheel where driving is mostly straight. In these examples, thetorque response of the wheel under various conditions (spoofing vs handsoff wheel vs hands on wheel will look very different) can bedifferentiated. Notably, depending on the spoofing object location, aconstant torque varying with wheel angle can be easily differentiated.It is assumed that the vehicle is providing a command to move the wheelunder various sinusoidal patterns of different rates but mostly drivingstraight. Also, in the HOFW condition, noise of the momentum of thewheel itself is not filtered, which will be 0 after filtering. This isto show the increasing momentum of the wheel due to a weight on thewheel when a spoofing device is attached.

Graph segment 402 illustrates the quasi-random torque values generatedby a human. Graph segment 404 illustrates torque responses over timewhen a spoofing event is occurring. The wave pattern is induced when theobject moves due to changes in wheel angle θ (magnitude of wheelturning) and wheel angle rate of change

$\frac{d\theta}{dt}$

(how fast the steering wheel is being turned). Segment 406 is related ofHOFW events, and segment 408 illustrates torque responses due to ADASautomated steering input.

FIG. 5 is a flowchart of an example method of the present disclosure.The method can include a step 502 of determining input torque valuesobtained from a steering torque sensor. It will be understood that thesetorque values are obtained over a period of time. For example, thetorque values can be obtained over one-minute increments, but other timeframes can also be used. In addition to using input torque values,torque statistics can also be used.

The method can include a step 504 of determining steering wheel anglevalues that are indicative of how sharply the steering wheel has beenturned. These steering wheel angle values are also obtained over therelevant time period used in step 502 and can be sensed using a steeringwheel angle sensor. The method can include a step 506 of determiningroad disturbances using a road disturbance model. As noted above, theroad disturbance model can be generated from vehicle sensors such ascameras, which can detect likely road disturbances such as holes, bumps,road topology, and the like. The road disturbance model can alsointegrate information from a map.

The method can include a step 508 of applying a driver model. As notedabove, the driver model can be created by training a model using one ormore human drivers. The behaviors of the drivers are analyzed over timeand are correlated with various road conditions and driving parametersand steering responses/styles can be identified. Various permutationsdata obtained from steps 502-508 can be used as input into a processorthat can apply an analytical model, neural network, gradient boostedtree, anomaly detection algorithm(s). The method includes a step 510 ofthe processor determining when the various inputs indicate whether thedriver is producing the torque and/or steering wheel anglevalues/changes or whether the torque and/or steering wheel anglevalues/changes are likely attributable to a spoof. In some instances,more particular information can be gathered. For example, the method caninclude a step 512 of determining a weight and/or location of a spoofingobject on the steering wheel. In some instances, the method can includea step 514 of inferring a type of the spoofing object. For example, awater bottle wedged into a steering wheel may produce different torqueand steering wheel angle values than a weight that is suspended from thesteering wheel. In some embodiments, the method can include a step 516of determining wheel angle values of the steering wheel over the periodof time, as well as a step 518 of determining wheel angle rate of changeof the steering wheel over the period of time. Again, the wheel anglevalues and the wheel angle rate of change can be used in determiningwhen input torque values are indicative of a spoof or human interaction.Thus, these steps can occur prior to step 510.

In step 520, the method includes a step of executing an automaticsteering response or remediation upon detection of the spoofing object.For example, when the spoofing object is detected, the vehicle canautomatically briefly overtake steering control to ensure that thevehicle stays centered in its lane, as well as outputting a warning tothe driver to remove the spoofing object. In some instances, only thewarning may be given to the driver. In yet other examples, anotherautomated response may be to slow the vehicle or cause the vehicle tomove onto a shoulder or into a parking location (such as when thevehicle is in an urban area).

Implementations of the systems, apparatuses, devices and methodsdisclosed herein may comprise or utilize a special purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more processors and system memory, as discussed herein.Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. An implementationof the devices, systems and methods disclosed herein may communicateover a computer network. A “network” is defined as one or more datalinks that enable the transport of electronic data between computersystems and/or modules and/or other electronic devices.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims may notnecessarily be limited to the described features or acts describedabove. Rather, the described features and acts are disclosed as exampleforms of implementing the claims.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentdisclosure. Thus, the breadth and scope of the present disclosure shouldnot be limited by any of the above-described exemplary embodiments butshould be defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the presentdisclosure. For example, any of the functionality described with respectto a particular device or component may be performed by another deviceor component. Conditional language, such as, among others, “can,”“could,” “might,” or “may,” unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments could include, while otherembodiments may not include, certain features, elements, and/or steps.Thus, such conditional language is not generally intended to imply thatfeatures, elements, and/or steps are in any way required for one or moreembodiments.

What is claimed is:
 1. A method comprising: determining input torquevalues obtained from a steering torque sensor associated with a steeringinput mechanism of a vehicle, wherein the input torque values areobtained over a period of time; determining road disturbances using aroad disturbance model; applying a driver model that is indicative ofhuman driver hands-on-wheel behaviors; determining when input torquevalues are indicative of a spoof or human interaction with the steeringinput mechanism using the input torque values, the road disturbancemodel, and the driver model; and executing a remediating measure whenthe input torque values are indicative of the spoof.
 2. The methodaccording to claim 1, further comprising generating the road disturbancemodel from sensor output obtained from a vehicle sensor platform thatcomprises at least one camera, the sensor output being images obtainedby the at least one camera.
 3. The method according to claim 1, furthercomprising: determining angle values of the steering input mechanismover the period of time; and determining an angle rate of change of thesteering input mechanism over the period of time, the angle values andthe angle rate of change being used in determining when input torquevalues are indicative of the spoof or human interaction.
 4. The methodaccording to claim 1, wherein the remediating measure includesdisplaying a warning message on a human-machine interface of thevehicle.
 5. The method according to claim 1, wherein the remediatingmeasure includes causing an advanced driver assistance system of thevehicle to slow the vehicle or deactivate a fully or semi-autonomousmode of steering.
 6. The method according to claim 1, wherein the inputtorque values are indicative of the human interaction when the inputtorque values are quasi-random and the input torque values areindicative of the spoof when the input torque values are cyclical. 7.The method according to claim 1, further comprising filtering noise fromthe input torque values, the noise being inferred from the roaddisturbance model, the noise being created from road and/orenvironmental conditions.
 8. The method according to claim 1, furthercomprising determining and applying vehicle acceleration values over theperiod of time.
 9. The method according to claim 1, further comprisingdetermining when the spoof is created by a device associated with thesteering input mechanism.
 10. A vehicle, comprising: a steering wheel ofthe vehicle; a steering torque sensor coupled to the steering wheel; andan advance driver assistance system (ADAS), comprising a processor andmemory, the processor executing instructions stored in the memory to:determine input torque values obtained from the steering torque sensorassociated with the steering wheel of the vehicle, wherein the inputtorque values are obtained over a period of time; determine roaddisturbances using a road disturbance model; apply a driver model thatis indicative of human driver hands-on-wheel behaviors; determine wheninput torque values are indicative of a spoof or human interaction withthe steering wheel using the input torque values, the road disturbancemodel, and the driver model; and execute a remediating measure when theinput torque values are indicative of the spoof.
 11. The vehicleaccording to claim 10, further comprising a vehicle sensor platform thatcomprises at least a camera, the ADAS being configured to generate theroad disturbance model from images obtained from the camera, the ADASusing a modeling engine that applies image processing logic to detectroad disturbances in the images.
 12. The vehicle according to claim 10,wherein the ADAS is configured to: determine wheel angle values of thesteering wheel over the period of time; and determine wheel angle rateof change of the steering wheel over the period of time, the wheel anglevalues and the wheel angle rate of change being used in determining wheninput torque values are indicative of the spoof or human interaction.13. The vehicle according to claim 10, wherein the remediating measureincludes the ADAS causing a warning message to be displayed on ahuman-machine interface of the vehicle.
 14. The vehicle according toclaim 10, wherein the remediating measure includes the ADAS slowing thevehicle or deactivate a fully or semi-autonomous mode of steering. 15.The vehicle according to claim 10, wherein the input torque values areindicative of the human interaction when the input torque values arequasi-random and the input torque values are indicative of the spoofwhen the input torque values are cyclical.
 16. The vehicle according toclaim 10, wherein the ADAS is configured to filter noise from the inputtorque values, the noise being inferred from the road disturbance model,the noise being created from road conditions.
 17. The vehicle accordingto claim 10, wherein the ADAS is configured to determine and applyvehicle acceleration values over the period of time.
 18. The vehicleaccording to claim 10, wherein the ADAS is configured to determine whenthe spoof is created by a device associated with the steering wheel. 19.A method comprising: determining input torque values obtained from asteering torque sensor associated with a steering wheel of a vehicle,wherein the input torque values are obtained over a period of time;filtering out a road disturbances using a road disturbance model, theroad disturbances introducing noise into the input torque values;applying a driver model that is indicative of human driverhands-on-wheel behaviors; determining wheel angle values of the steeringwheel over the period of time; determining wheel angle rate of change ofthe steering wheel over the period of time, the wheel angle values andthe wheel angle rate of change being used in determining when inputtorque values are indicative of a spoof or human interaction determiningwhen input torque values are indicative of the spoof or humaninteraction with the steering wheel using the input torque values, theroad disturbance model, and the driver model, the wheel angle values,and the wheel angle rate of change; and executing a remediating measurewhen the input torque values are indicative of the spoof.
 20. The methodaccording to claim 19, wherein the remediating measure includes slowingthe vehicle or deactivate a fully or semi-autonomous mode of steering.